DFES comparison tool

A tool to compare future energy scenario data (FES/DFES). Provide CSV files (they can be the same), select a scenario from each, and get a dashboard showing how they compare.

Try these examples:

Notes

Usage

Use the file selectors to pick CSV files - you can use the same file in each - by either clicking "Select a CSV file" or dragging-and-dropping a file onto it. Next, select a scenario for each one. Note that different years generally can have different scenario definitions. You will then be presented with a dashboard of graphs showing how predictions change over time. You can also choose to limit to a specific licence area (as named in a particular source CSV) and/or limit to a particular grid supply point (GSP). This should let you compare two scenarios in the same FES/DFES, scenarios in different FES/DFES files, different GSPs, or different licence areas.

CSV files

The CSV files stay within your browser and aren't uploaded anywhere. They should be flat files containing rows by scenario + building block + GSP. They should have columns for "Licence area", "Scenario name", "Building Block ID" and "Grid Supply Point". We will attempt to fuzzy-match for those names to deal with the different ways NESO and the DNOs label or spell things e.g. "licence" vs "license". The matching column headings will be displayed in the file selection box. Each row should also have columns for each year's value of that specific building block/GSP/scenario.

Some example CSV files: DFES 2024, DFES 2023 and DFES 2022, NESO FES 2025.

Example table with the columns that are used by the tool highlighted:

Licence Area	Voltage Level	Scenario Name in DFES	Output Parameter	Grid Supply Point in DFES	FES Scenario	Building Block ID Number	Unit	DNO Licence area	GSP in NG FES	2023	2024	2025	2026	2027	2028	2029	2030	2031	2032	2033	2034	2035	2036	2037	2038	2039	2040	2041	2042	2043	2044	2045	2046	2047	2048	2049	2050
NPgN	GSP	NPg Reference Scenario	Number of domestic customers	BLYTH 132kV	(Not in FES) NPg Reference Scenario	Dem_BB001a	Number of	NPgN	Blyth 132kV	28872	29081	29290	29500	29709	29861	30014	30167	30319	30472	30622	30772	30922	31072	31222	31412	31602	31793	31983	32173	32448	32724	32999	33274	33550	33825	34101	34376
NPgN	GSP	NPg Reference Scenario	Number of domestic customers	BLYTH 66kV	(Not in FES) NPg Reference Scenario	Dem_BB001a	Number of	NPgN	Blyth 66kV	105147	106022	106896	107771	108646	109343	110041	110738	111435	112133	112620	113108	113596	114084	114572	115460	116349	117237	118126	119014	120126	121238	122350	123462	124574	125686	126798	127909
NPgN	GSP	NPg Reference Scenario	Number of domestic customers	FOURSTONES	(Not in FES) NPg Reference Scenario	Dem_BB001a	Number of	NPgN	Fourstones	8403	8421	8439	8457	8475	8488	8501	8514	8527	8539	8552	8564	8577	8590	8602	8618	8635	8651	8667	8684	8708	8733	8757	8781	8806	8830	8854	8879

Totals

Each dashboard panel shows the formula that was used to calculate the line charts. If you have selected all GSPs then each individual building block is first summed up over all GSPs before calculating the final value. This ensures the correct nominator/denominator maths for more complex values such as "EVs per customer". If a licence area is selected then only matching GSPs in that licence area will be used in the calculations.

Year slider

The year slider can be used to change the year range on all the graphs.

Saving the results

On each dashboard panel you can click the CSV button to save the data for a specific graph. This will give a row per series with columns for each year. These values may have had some rounding. The names of each series are created from the file names, scenario, and GSP selection

On each dashboard panel you can click the SVG button to save a vector graphic of the graph.

If you choose to print the page we remove unnessary parts of the page and optimise the graphs to use the full page width.

Environmental impact of this tool

This tool has been designed to have a minimal carbon impact by keeping it small; the whole page is under 120kB in size (not including any data you choose to load). This means that it produces around 0.01g of CO2 per visit A+ if you use local CSV files. It also means it should be fast to use.